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Challenges in microbial ecology: building predictive understanding of community function and dynamics

Challenges in microbial ecology: building predictive understanding of community function and dynamics
Challenges in microbial ecology: building predictive understanding of community function and dynamics
he importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth’s soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model–experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved.
1751-7362
1-12
Widder, Stefanie
502c93d6-777e-48cf-bb50-12d443a3f1ee
Allen, Rosalind J.
ea4c5cb8-7440-4d90-a138-39393a356a50
Pfeiffer, Thomas
d3d4dad0-924f-4bc2-9d95-ffc76cb861a2
Hoyle, Rebecca B.
e980d6a8-b750-491b-be13-84d695f8b8a1
Soyer, Orkun
f4676f2b-0357-44db-9b3f-0e6a9b43dded
Isaac Newton Institute Fellows
Widder, Stefanie
502c93d6-777e-48cf-bb50-12d443a3f1ee
Allen, Rosalind J.
ea4c5cb8-7440-4d90-a138-39393a356a50
Pfeiffer, Thomas
d3d4dad0-924f-4bc2-9d95-ffc76cb861a2
Hoyle, Rebecca B.
e980d6a8-b750-491b-be13-84d695f8b8a1
Soyer, Orkun
f4676f2b-0357-44db-9b3f-0e6a9b43dded

Widder, Stefanie and Soyer, Orkun , Isaac Newton Institute Fellows (2016) Challenges in microbial ecology: building predictive understanding of community function and dynamics. The ISME Journal, 1-12. (doi:10.1038/ismej.2016.45). (PMID:27022995)

Record type: Article

Abstract

he importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth’s soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model–experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved.

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More information

Accepted/In Press date: 22 February 2016
Published date: 29 March 2016
Organisations: Applied Mathematics

Identifiers

Local EPrints ID: 393241
URI: http://eprints.soton.ac.uk/id/eprint/393241
ISSN: 1751-7362
PURE UUID: 145e3af1-5969-42aa-af17-cd92e7e268e8
ORCID for Rebecca B. Hoyle: ORCID iD orcid.org/0000-0002-1645-1071

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Date deposited: 22 Apr 2016 10:25
Last modified: 15 Mar 2024 03:36

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Contributors

Author: Stefanie Widder
Author: Rosalind J. Allen
Author: Thomas Pfeiffer
Author: Orkun Soyer
Corporate Author: Isaac Newton Institute Fellows

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